Concern has been expressed in the Workers’ Comp industry about its aging workforce. Workers, especially claims reps with years of experience are retiring in greater numbers leaving a semi-vacuum in their place. This leads to a pressing need for training and providing tools that will help workers make smart decisions well before they earn seniority. Analytics is one of the tools that can make workers smarter.
Most people say they want analytics. Yet, many are not sure what analytics is or what benefits will be gained. Some anticipate operational disruption and fear the cost, neither of which is necessarily well-founded. Whether it is because of lack of knowledge or fear of change, many still hold back. To learn more about how analytics and how it can impact medical management in Workers’ Comp, please read, “Making the Most of Analytics to Improve Medical Outcomes”
The reason for implementing analytics of any variety is to gain knowledge about the organization by analyzing its data. The knowledge gained should be actionable knowledge, meaning it supports intelligent action and decisions while enlightening the way forward.
Learning more about the business provides information for decision support which can be applied in an organization from long term planning to transactional decision-making by frontline workers. Transactional decision support at the operational level is where knowledge gained from analytics makes workers smarter.
Transactional decision support
Analytics-informed transactional decision support is linking knowledge gained through analytics with operations. Analytics will have no impact on the organization, its clients, or its workers unless the information is driven to, and acted upon at the operational level. Moreover, the information must be presented to workers in a fashion that guides them to appropriate action.
The way workers receive information determines how the knowledge gained through analytics is acted upon. In other words, the system designed to deliver appropriate knowledge to the right workers at the right time is crucial. Information designed to generate action can be delivered at the right time in the form of electronic alerts. But alerts must contain all the information needed to take appropriate action, the correct action.
Smart information delivery
For example, alerts transmitted to claims reps prompting them to adjust medical reserves in a claim must contain all the information necessary for adjusting reserves accurately. Claim background information as well as the conditions found through claim data monitoring that generated the alert should be portrayed for them. Importantly, to enlighten claims reps further, the alert should display the probable ultimate medical reserve amount for the claim based on predictive analytics.
Similarly, information should be delivered to others in the organization who would benefit from the knowledge in managing the claim. The conditions that initiated the alert to claims reps regarding the need for medical loss reserve adjustment are often appropriate for nurse case management involvement as well. The system designed for information delivery can automatically notify nurse case managers along with claim reps, thereby coordinating initiatives leading to early claim resolution.
Analytics-informed transactional decision support transmitted to the operational level accrues additional benefits. Not only are frontline workers smarter, but responses are more timely and consistent leading to credible measures of savings.
Measures of savings
Continuing the example of using predictive analytics to alert for medical loss reserving, even more value can be gained. Objective medical savings can be calculated at claim closure based on reserve projections and real-time proactive claims handling by claims reps and coordinated medical intervention by nurses. Early intervention by informed workers will lead to measurable savings that can be communicated to clients and other constituents.
Frontline workers can be made smarter and more efficient through technology. Driving information gained from predictive analytics to the transaction level makes workers more accurate and efficient. Even minimally experienced workers given the right information at the right time will make accurate and timely decisions. Moreover, experienced workers will elevate their accuracy and efficiency, thereby saving time and money for the organization while improving claim outcomes.